用GAN提升电容层析成像重建精度，让图像更清晰、边界更锐利。
Image-to-Image Translation with Generative Adversarial Network for Electrical Resistance Tomography Reconstruction
- 采用Pix2Pix GAN学习从低质图像到高质图像的映射关系。
- 在模拟与实验数据上，SSIM、PSNR等指标显著提升。
- 适合需要高分辨率成像的工业过程监测场景。
电学层析成像技术因其非侵入性、本质安全性和低成本，被广泛用于多相流监测。然而，传统重建方法难以捕捉细微结构，限制了其进一步应用。受深度学习进展启发，本文引入Pix2Pix生成对抗网络（GAN）来提升电容层析成像（ECT）的图像重建质量。构建了全面的模拟与实验数据库，并实现了多种基准重建算法作为对比。所提GAN在定量指标如SSIM、PSNR和PMSE上均有明显改善，定性上生成了高分辨率图像，边界清晰，不再受网格离散化限制。
原文摘要 · Abstract (English)
Electrical tomography techniques have been widely employed for multiphase-flow monitoring owing to their non invasive nature, intrinsic safety, and low cost. Nevertheless, conventional reconstructions struggle to capture fine details, which hampers broader adoption. Motivated by recent advances in deep learning, this study introduces a Pix2Pix generative adversarial network (GAN) to enhance image reconstruction in electrical capacitance tomography (ECT). Comprehensive simulated and experimental databases were established and multiple baseline reconstruction algorithms were implemented. The proposed GAN demonstrably improves quantitative metrics such as SSIM, PSNR, and PMSE, while qualitatively producing high resolution images with sharp boundaries that are no longer constrained by mesh discretization.
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